We’ve just collected some data! Now let’s explore it!

In this short exercise you will create exploratory data visualizations using the data we just collected, by:

  1. uncommenting lines of code (remove # signs)
  2. completing the code blanks (___)

Remember you do not need to commit or push your work! Just complete the code and Knit to save your changes and update the html output.

The goal of this exercise is to become more comfortable using the ggplot() function.

Packages

We will need the ggplot2 package, which is contained in the tidyverse.

# library(___)

Data

Next let’s load the class data. Look for the .csv file in the current e-03a-class-poll folder:

# poll_data <- read.csv("___")

Let’s take a look at the dimensions (rows and columns), and variables (column names):

# glimpse(___)

Data Dictionary

The dataframe contains ___ observations and ___ columns. Complete the data dictionary below.

The variables are:

Exploratory Plots

So far you have seen scatter plots created using the geom_point() function. However, scatter plots are unlikely to be informative for these survey data because the questions are unrelated.

You will instead need to use the geom_histogram() function, which uses one aesthetic mapping to the x-axis to create a frequency plot (histogram) of the data.

Plot 1: House moves

Complete the code chunk below to plot a simple histogram of the number of house moves:

# ggplot(data = poll_data, aes(x = ___)) +
#   geom_histogram()

Plot 2: Pets owned

Complete the code chunk below to plot a histogram of the number of pets owned, and this time add an informative title and axis labels using labs().

# ggplot(data = poll_data, aes(x = ___)) +
#   geom_histogram() +
#   labs(title = "Number of Pets Owned by EAES 494 Students",
#        x = "___",
#        y = "Count")

Plot 3: International flights traveled

Complete the code chunk below to plot a histogram of the number of international flights traveled:

# ggplot(data = ___, aes(x = ___)) +
#   geom_histogram() +
#   labs(title = "___",
#        x = "___",
#        y = "___")

Plot 4: Credit-hours enrollment

Complete the code chunk below to plot a histogram of the number of credit hours enrolled in and this time clean up the look of the plot by adding the theme layer named theme_bw()

# ggplot(data = ___, aes(x = ___)) +
#   geom_histogram() +
#   labs(title = "___",
#        x = "___",
#        y = "___")

Plot 5: Estimate of the number of moons in our solar system

Complete the code chunk below to plot a histogram of the student-estimated number of moons in our solar system, and this time add a theme layer of your choice (from list that appears as you start to type theme).

# ggplot(data = ___, aes(x = ___)) +
#   geom_histogram() +
#   labs(title = "___",
#        x = "___",
#        y = "___")